IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v70y2014i4p812-822.html
   My bibliography  Save this article

Identifying functional co-activation patterns in neuroimaging studies via poisson graphical models

Author

Listed:
  • Wenqiong Xue
  • Jian Kang
  • F. DuBois Bowman
  • Tor D. Wager
  • Jian Guo

Abstract

No abstract is available for this item.

Suggested Citation

  • Wenqiong Xue & Jian Kang & F. DuBois Bowman & Tor D. Wager & Jian Guo, 2014. "Identifying functional co-activation patterns in neuroimaging studies via poisson graphical models," Biometrics, The International Biometric Society, vol. 70(4), pages 812-822, December.
    • Handle: RePEc:bla:biomet:v:70:y:2014:i:4:p:812-822
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/biom.12216
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Dimitris Karlis, 2003. "An EM algorithm for multivariate Poisson distribution and related models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(1), pages 63-77.
    2. Yang, Ying & Kang, Jian, 2010. "Joint analysis of mixed Poisson and continuous longitudinal data with nonignorable missing values," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 193-207, January.
    3. Kang, Jian & Johnson, Timothy D. & Nichols, Thomas E. & Wager, Tor D., 2011. "Meta Analysis of Functional Neuroimaging Data via Bayesian Spatial Point Processes," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 124-134.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kalema, George & Molenberghs, Geert, 2016. "Generating Correlated and/or Overdispersed Count Data: A SAS Implementation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 70(c01).
    2. Guo-Liang Tian & Xiqian Ding & Yin Liu & Man-Lai Tang, 2019. "Some new statistical methods for a class of zero-truncated discrete distributions with applications," Computational Statistics, Springer, vol. 34(3), pages 1393-1426, September.
    3. Chan, Jennifer So Kuen & Wan, Wai Yin, 2014. "Multivariate generalized Poisson geometric process model with scale mixtures of normal distributions," Journal of Multivariate Analysis, Elsevier, vol. 127(C), pages 72-87.
    4. Tom Brijs & Dimitris Karlis & Filip Van den Bossche & Geert Wets, 2007. "A Bayesian model for ranking hazardous road sites," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(4), pages 1001-1017, October.
    5. Yang, Ying & Kang, Jian, 2010. "Joint analysis of mixed Poisson and continuous longitudinal data with nonignorable missing values," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 193-207, January.
    6. Silvia Montagna & Tor Wager & Lisa Feldman Barrett & Timothy D. Johnson & Thomas E. Nichols, 2018. "Spatial Bayesian latent factor regression modeling of coordinate†based meta†analysis data," Biometrics, The International Biometric Society, vol. 74(1), pages 342-353, March.
    7. Bora Çekyay & J.B.G. Frenk & Sonya Javadi, 2023. "On Computing the Multivariate Poisson Probability Distribution," Methodology and Computing in Applied Probability, Springer, vol. 25(3), pages 1-22, September.
    8. Kus, Coskun, 2007. "A new lifetime distribution," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4497-4509, May.
    9. Avanzi, Benjamin & Taylor, Greg & Vu, Phuong Anh & Wong, Bernard, 2016. "Stochastic loss reserving with dependence: A flexible multivariate Tweedie approach," Insurance: Mathematics and Economics, Elsevier, vol. 71(C), pages 63-78.
    10. Sobom M. Somé & Célestin C. Kokonendji & Nawel Belaid & Smail Adjabi & Rahma Abid, 2023. "Bayesian local bandwidths in a flexible semiparametric kernel estimation for multivariate count data with diagnostics," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(3), pages 843-865, September.
    11. Robert C. Jung & Andrew R. Tremayne, 2020. "Maximum-Likelihood Estimation in a Special Integer Autoregressive Model," Econometrics, MDPI, vol. 8(2), pages 1-15, June.
    12. Rolf Larsson, 2022. "Bartlett correction of an independence test in a multivariate Poisson model," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 76(4), pages 391-417, November.
    13. Emily B. Dennis & Byron J.T. Morgan & Martin S. Ridout, 2015. "Computational aspects of N-mixture models," Biometrics, The International Biometric Society, vol. 71(1), pages 237-246, March.
    14. Su Pei-Fang & Mau Yu-Lin & Li Chung-I & Guo Yan & Liu Qi & Shyr Yu & Boice John D., 2017. "Bivariate Poisson models with varying offsets: an application to the paired mitochondrial DNA dataset," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 16(1), pages 47-58, March.
    15. Juliana Schulz & Christian Genest & Mhamed Mesfioui, 2021. "A multivariate Poisson model based on comonotonic shocks," International Statistical Review, International Statistical Institute, vol. 89(2), pages 323-348, August.
    16. Rolf Larsson, 2020. "Discrete factor analysis using a dependent Poisson model," Computational Statistics, Springer, vol. 35(3), pages 1133-1152, September.
    17. Vicente G. Cancho & Dipak K. Dey & Francisco Louzada, 2016. "Unified multivariate survival model with a surviving fraction: an application to a Brazilian customer churn data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(3), pages 572-584, March.
    18. Lillestøl, Jostein, 2020. "Sampling risk evaluations in a tax fraud case: Some modelling issues," Discussion Papers 2020/5, Norwegian School of Economics, Department of Business and Management Science.
    19. Xuerong Chen & Guoqing Diao & Jing Qin, 2020. "Pseudo likelihood‐based estimation and testing of missingness mechanism function in nonignorable missing data problems," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(4), pages 1377-1400, December.
    20. Cholaquidis, Alejandro & Forzani, Liliana & Llop, Pamela & Moreno, Leonardo, 2017. "On the classification problem for Poisson point processes," Journal of Multivariate Analysis, Elsevier, vol. 153(C), pages 1-15.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:biomet:v:70:y:2014:i:4:p:812-822. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.